"""Congress"""

from typing import List

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
    "party",
    "pro_handicapped_infants",
    "pro_water_project_cost_sharing",
    "pro_adoption_of_the_budget_resolution",
    "pro_physician_fee_freeze",
    "pro_el_salvador_aid",
    "pro_religious_groups_in_schools",
    "pro_anti_satellite_test_ban",
    "pro_aid_to_nicaraguan_contras",
    "pro_mx_missile",
    "pro_immigration",
    "pro_synfuels_corporation_cutback",
    "pro_education_spending",
    "pro_superfund_right_to_sue",
    "pro_crime",
    "pro_duty_free_exports",
    "pro_export_administration_act_south_africa",
]

DESCRIPTION = "Congress dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Congress"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/105/congressional+voting+records")
_CITATION = """
@misc{misc_congressional_voting_records_105,
  title        = {{Congressional Voting Records}},
  year         = {1987},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5C01P}}
}"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/congress/raw/main/house-votes-84.data"
}
features_types_per_config = {
    "voting": {
        "pro_handicapped_infants": datasets.Value("bool"),
        "pro_water_project_cost_sharing": datasets.Value("bool"),
        "pro_adoption_of_the_budget_resolution": datasets.Value("bool"),
        "pro_physician_fee_freeze": datasets.Value("bool"),
        "pro_el_salvador_aid": datasets.Value("bool"),
        "pro_religious_groups_in_schools": datasets.Value("bool"),
        "pro_anti_satellite_test_ban": datasets.Value("bool"),
        "pro_aid_to_nicaraguan_contras": datasets.Value("bool"),
        "pro_mx_missile": datasets.Value("bool"),
        "pro_immigration": datasets.Value("bool"),
        "pro_synfuels_corporation_cutback": datasets.Value("bool"),
        "pro_education_spending": datasets.Value("bool"),
        "pro_superfund_right_to_sue": datasets.Value("bool"),
        "pro_crime": datasets.Value("bool"),
        "pro_duty_free_exports": datasets.Value("bool"),
        "pro_export_administration_act_south_africa": datasets.Value("bool"),
        "party": datasets.ClassLabel(num_classes=2, names=("democrat", "republican")),
    }
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


class CongressConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(CongressConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Congress(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "voting"
    BUILDER_CONFIGS = [
        CongressConfig(name="voting",
                    description="Binary classification of politician, either democrat or republican.")
    ]


    def _info(self):
        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
                                    features=features_per_config[self.config.name])

        return info
    
    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        downloads = dl_manager.download_and_extract(urls_per_split)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
        ]
    
    def _generate_examples(self, filepath: str):
        data = pandas.read_csv(filepath, header=None)
        data = self.preprocess(data, config=self.config.name)

        for row_id, row in data.iterrows():
            data_row = dict(row)

            yield row_id, data_row


    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
        data.columns = _BASE_FEATURE_NAMES
        vote_dictionary = {
            "y": "pro",
            "n": "against",
            "?": "did_not_vote",
        }
        for feature in _BASE_FEATURE_NAMES[1:]:
            data.loc[:, feature] = data[feature].apply(lambda x: vote_dictionary[x])
        data.loc[:, "party"] = data["party"].apply(lambda x: 0 if x == "democrat" else 1)
        data = data.astype({"party": "int8"})

        data = data[list(features_types_per_config[config].keys())]

        return data